Structure Determination Of Protein Complexes Research Articles

Three-dimensional structure determination of protein complexes using matrix-landing mass spectrometry

Native mass spectrometry (MS) is increasingly used to provide complementary data to electron microscopy (EM) for protein structure characterization. Beyond the ability to provide mass measurements of gas-phase biomolecular ions, MS instruments offer the ability to purify, select, and precisely control the spatial location of these ions. Here we present a modified Orbitrap MS system capable of depositing a native MS ion beam onto EM grids. We further describe the use of a chemical landing matrix that preserves the structural integrity of the deposited particles. With this system we obtain a three-dimensional reconstruction of the 800 kDa protein complex GroEL from gas-phase deposited GroEL ions. These data provide direct evidence that non-covalent protein complexes can indeed retain their condensed-phase structures following ionization and vaporization. Finally, we describe how further developments of this technology could pave the way to an integrated MS-EM technology with promise to provide improved cryo-EM sample preparation over conventional plunge-freezing techniques.

Natural variant frequencies across domains from different sarcomere proteins cross-correlate to identify inter-protein contacts associated with cardiac muscle function and disease

Coordinated sarcomere proteins produce contraction force for muscle shortening. In human ventriculum they include the cardiac myosin motor (βmys), repetitively converting ATP free energy into work, and myosin binding protein C (MYBPC3) that in complex with βmys is regulatory. Single nucleotide variants (SNVs) causing hereditary heart diseases frequently target this protein pair. The βmys/MYBPC3 complex models a regulated motor and is used here to study how the proteins couple. SNVs in βmys or MYBPC3 survey human populations worldwide. Their protein expression modifies domain structure affecting phenotype and pathogenicity outcomes. When the SNV modified domain locates to inter-protein contacts it could affect complex coordination. Domains involved, one in βmys the other in MYBPC3, form coordinated domains (co-domains). Co-domain bilateral structure implies the possibility for a shared impact from SNV modification in either domain suggesting a correlated response to a common perturbation could identify their location. Genetic divergence over human populations is proposed to perturb SNV probability coupling that is detected by cross-correlation in 2D correlation genetics (2D-CG). SNV probability data and 2D-CG identify three critical sites, two in MYBPC3 with links to several domains across the βmys motor, and, one in βmys with links to the MYBPC3 regulatory domain. MYBPC3 sites are hinges sterically enabling regulatory interactions with βmys. The βmys site is the actin binding C-loop (residues 359-377). The C-loop is a trigger for actin-activated myosin ATPase and a contraction velocity modulator. Co-domain identification implies their spatial proximity suggesting a novel approach for in vivo protein complex structure determination.

DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes

Information about macromolecular structure of protein complexes and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automated deep learning-based method for fast de novo multichain protein complex structure determination from high-resolution cryoelectron microscopy (cryo-EM) maps. We applied DeepTracer on a previously published set of 476 raw experimental cryo-EM maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer, and the rmsd value improved from 1.29 Å to 1.18 Å. Additionally, we applied DeepTracer on a set of 62 coronavirus-related cryo-EM maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average rmsd of 0.93 Å. Additional tests with related methods further exemplify DeepTracer's competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only 2 h. The web service is globally accessible at https://deeptracer.uw.edu.

Genetic interaction mapping informs integrative structure determination of protein complexes.

Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

Cryo-EM Grid Preparation of Membrane Protein Samples for Single Particle Analysis.

Recent advances in cryo-electron microscopy (cryo-EM) have made it possible to solve structures of biological macromolecules at near atomic resolution. Development of more stable microscopes, improved direct electron detectors and faster software for image processing has enabled structural solution of not only large macromolecular (megadalton range) complexes but also small (~60 kDa) proteins. As a result of the widespread use of the technique, we have also witnessed new developments of techniques for cryo-EM grid preparation of membrane protein samples. This includes new types of solubilization strategies that better stabilize these protein complexes and the development of new grid supports with proven efficacy in reducing the motion of the molecules during electron beam exposure. Here, we discuss the practicalities and recent challenges of membrane protein sample preparation and vitrification, as well as grid support and foil treatment in the context of the structure determination of protein complexes by single particle cryo-EM.

Integrative Structure Determination of Protein Complexes by Inferred Structural Equivalence

Integrative Structure Determination of Protein Complexes by Inferred Structural Equivalence

Sparsely-sampled, high-resolution 4-D omit spectra for detection and assignment of intermolecular NOEs of protein complexes.

Unambiguous detection and assignment of intermolecular NOEs are essential for structure determination of protein complexes by NMR. Such information has traditionally been obtained with 3-D half-filtered experiments, where scalar coupling-based purging of intramolecular signals allows for selective detection of intermolecular NOEs. However, due to the large variation of (1)JHC scalar couplings and limited chemical shift dispersion in the indirect proton dimension, it is difficult to obtain reliable and complete assignments of interfacial NOEs. Here, we demonstrate a strategy that combines selective labeling and high-resolution 4-D NOE spectroscopy with sparse sampling for reliable identification and assignment of intermolecular NOEs. Spectral subtraction of component-labeled complexes from a uniformly-labeled protein complex yields an "omit" spectrum containing positive intermolecular NOEs with little signal degeneracy. Such a strategy can be broadly applied to unbiased detection, assignment and presentation of intermolecular NOEs of protein complexes.

¹H, ¹³C, and ¹⁵N backbone resonance assignments of the 37 kDa voltage-gated Ca²⁺ channel β4 subunit core SH3-GK domains.

The β subunit of the voltage-gated Ca(2+) channel (α1, α2δ, and β subunits) is a member of the MAGUK family of proteins and plays an essential role in regulating Ca(2+) channel trafficking and gating. It also serves as a central interaction partner for various Ca(2+) channel regulatory proteins. We report here the nearly complete (1)H, (13)C, and (15)N backbone resonance assignments of the 37kDa core SH3-GK domains of the β4 subunit. This is the first report of solution assignments for β subunits, and as such will lay the foundation for future investigations of interaction site mapping, functional dynamics, and protein complex structure determination.

Protein Secondary Structure Determination by Constrained Single-Particle Cryo-Electron Tomography

Cryo-electron microscopy (cryo-EM) is a powerful technique for 3D structure determination of protein complexes by averaging information from individual molecular images. The resolutions that can be achieved with single-particle cryo-EM are frequently limited by inaccuracies in assigning molecular orientations based solely on 2D projection images. Tomographic data collection schemes, however, provide powerful constraints that can be used to more accurately determine molecular orientations necessary for 3D reconstruction. Here, we propose "constrained single-particle tomography" as a general strategy for 3D structure determination in cryo-EM. A key component of our approach is the effective use of images recorded in tilt series to extract high-resolution information and correct for the contrast transfer function. By incorporating geometric constraints into the refinement to improve orientational accuracy of images, we reduce model bias and overrefinement artifacts and demonstrate that protein structures can be determined at resolutions of ∼8Å starting from low-dose tomographic tilt series.

Convenient method for resolving degeneracies due to symmetry of the magnetic susceptibility tensor and its application to pseudo contact shift-based protein-protein complex structure determination.

Pseudo contact shifts (PCSs) induced by paramagnetic lanthanide ions fixed in a protein frame provide long-range distance and angular information, and are valuable for the structure determination of protein–protein and protein–ligand complexes. We have been developing a lanthanide-binding peptide tag (hereafter LBT) anchored at two points via a peptide bond and a disulfide bond to the target proteins. However, the magnetic susceptibility tensor displays symmetry, which can cause multiple degenerated solutions in a structure calculation based solely on PCSs. Here we show a convenient method for resolving this degeneracy by changing the spacer length between the LBT and target protein. We applied this approach to PCS-based rigid body docking between the FKBP12-rapamycin complex and the mTOR FRB domain, and demonstrated that degeneracy could be resolved using the PCS restraints obtained from two-point anchored LBT with two different spacer lengths. The present strategy will markedly increase the usefulness of two-point anchored LBT for protein complex structure determination.Electronic supplementary materialThe online version of this article (doi:10.1007/s10858-012-9623-8) contains supplementary material, which is available to authorized users.

Structure-based evaluation ofin silicopredictions of protein–protein interactions using Comparative Docking

Due to the limitations in experimental methods for determining binary interactions and structure determination of protein complexes, the need exists for computational models to fill the increasing gap between genome sequence information and protein annotation. Here we describe a novel method that uses structural models to reduce a large number of in silico predictions to a high confidence subset that is amenable to experimental validation. A two-stage evaluation procedure was developed, first, a sequence-based method assessed the conservation of protein interface patches used in the original in silico prediction method, both in terms of position within the primary sequence, and in terms of sequence conservation. When applying the most stringent conditions it was found that 20.5% of the data set being assessed passed this test. Secondly, a high-throughput structure-based docking evaluation procedure assessed the soundness of three dimensional models produced for the putative interactions. Of the data set being assessed, 8264 interactions or over 70% could be modelled in this way, and 27% of these can be considered 'valid' by the applied criteria. In all, 6.9% of the interactions passed both the tests and can be considered to be a high confidence set of predicted interactions, several of which are described. http://bioinformatics.leeds.ac.uk/~bmb4sjc. Supplementary data are available at Bioinformatics online.

Data‐driven docking: HADDOCK's adventures in CAPRI

We have shown previously that given high-resolution structures of the unbound molecules, structure determination of protein complexes is possible by including biochemical and/or biophysical data as highly ambiguous distance restraints in a docking approach. We applied this method, implemented in the HADDOCK (High Ambiguity Driven DOCKing) package (Dominguez et al., J Am Chem Soc 2003;125:1731-1737), to the targets in the fourth and fifth rounds of CAPRI. Here we describe our results and analyze them in detail. Special attention is given to the role of flexibility in our docking method and the way in which this improves the docking results. We describe extensions to our approach that were developed as a direct result of our participation in CAPRI. In addition to experimental information, we also included interface residue predictions from PPISP (Protein-Protein Interaction Site Predictor; Zhou and Shan, Proteins 2001;44:336-343), a neural network method. Using HADDOCK we were able to generate acceptable structures for 6 of the 8 targets, and to submit at least 1 acceptable structure for 5 of them. Of these 5 submissions, 3 were of medium quality (Targets 10, 11, and 15) and 2 of high quality (Targets 13 and 14). In all cases, predictions were obtained containing at least 40% of the correct epitope at the interface for both ligand and receptor simultaneously.

Structure determination of protein complexes by NMR.

This chapter describes nuclear magnetic resonance (NMR) methods that can be used to determine the structures of protein complexes. Many of these techniques are also applicable to other systems (e.g., protein-nucleic acid complexes). In the first section, we discuss methodologies for optimizing the sample conditions for the study of complexes. This is followed by a description of the methods that can be used to map interfaces when a full structure determination of the complex is not appropriate or not possible. We then describe experimental approaches for resonance assignment in complexes, these are essentially the same as those for isolated proteins. Subheading 6. describes the different types of so-called X-filtered NMR experiments that have been devised to separate and selectively observe either inter- or intramolecular structural information. These filtered NMR experiments are then exploited in the experimental strategies for structure determination of either protein complexes or homodimeric proteins. This is followed by a description of the calculation of their structures. Finally, we present case studies from three projects carried out in our laboratory, where we successfully used the methods presented in this chapter.

Interaction profile-based protein classification of death domain

BackgroundThe increasing number of protein sequences and 3D structure obtained from genomic initiatives is leading many of us to focus on proteomics, and to dedicate our experimental and computational efforts on the creation and analysis of information derived from 3D structure. In particular, the high-throughput generation of protein-protein interaction data from a few organisms makes such an approach very important towards understanding the molecular recognition that make-up the entire protein-protein interaction network. Since the generation of sequences, and experimental protein-protein interactions increases faster than the 3D structure determination of protein complexes, there is tremendous interest in developing in silico methods that generate such structure for prediction and classification purposes. In this study we focused on classifying protein family members based on their protein-protein interaction distinctiveness. Structure-based classification of protein-protein interfaces has been described initially by Ponstingl et al. [1] and more recently by Valdar et al. [2] and Mintseris et al. [3], from complex structures that have been solved experimentally. However, little has been done on protein classification based on the prediction of protein-protein complexes obtained from homology modeling and docking simulation.ResultsWe have developed an in silico classification system entitled HODOCO (Homology modeling, Docking and Classification Oracle), in which protein Residue Potential Interaction Profiles (RPIPS) are used to summarize protein-protein interaction characteristics. This system applied to a dataset of 64 proteins of the death domain superfamily was used to classify each member into its proper subfamily. Two classification methods were attempted, heuristic and support vector machine learning. Both methods were tested with a 5-fold cross-validation. The heuristic approach yielded a 61% average accuracy, while the machine learning approach yielded an 89% average accuracy.ConclusionWe have confirmed the reliability and potential value of classifying proteins via their predicted interactions. Our results are in the same range of accuracy as other studies that classify protein-protein interactions from 3D complex structure obtained experimentally. While our classification scheme does not take directly into account sequence information our results are in agreement with functional and sequence based classification of death domain family members.